Biomedical subjects

Xiaomei Ren

Publications and source records attributed to Xiaomei Ren.

6 recordsLinked to original sources

PubMed2026

A regulator of amino acid catabolism controls Acinetobacter baumannii gut colonization.

Asymptomatic gut colonization increases the risk of clinical infection and transmission by the multidrug-resistant pathogen Acinetobacter baumannii. Ornithine utilization was shown to be critical for A. baumannii competition with the resident microbiota to persist in gut colonization, but the regulatory mechanisms and cues are unknown. Here, we identify a transcriptional regulator, AstR, that specifically activates the expression of the A. baumannii ornithine utilization operon astNOP. Phylogenetic analysis suggests that AstR was co-opted from the Acinetobacter arginine utilization ast(G)CADBE locus and is specialized to regulate ornithine utilization in A. baumannii. Reporter assays showed that astN promoter expression was activated by ornithine but inhibited by glutamate and other preferred amino acids. astN promoter expression was similarly activated by incubation with fecal samples from conventional mice but not germ-free mice, suggesting AstR-dependent activation of the astN promoter responds to intermicrobial competition for amino acids. Finally, AstR was required for A. baumannii to colonize the gut in a mouse model. Together, these results suggest that pathogenic Acinetobacter species evolved AstR to regulate ornithine catabolism, which is required to compete with the microbiota during gut colonization.

Animals↗

PubMed2006

Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials.

We have studied methods for noise reduction of myoelectric signals and for extraction of motor unit action potentials from these signals. Effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet filtering method and threshold estimation calculated using wavelet transform to suppress background noise and Gaussian white noise. The technique was applied to single-channel, short-period real myoelectric signals from normal subjects and to artificially generated EMG recordings. In contrast to existing methods based on amplitude single-threshold filtering of the original myoelectric signal or a conventional digitally filtered signal, our technique is fast and robust. Moreover, the proposed algorithm is substantially automatic. The performance has been evaluated with a set of synthetic and experimentally recorded myoelectric signals. The basic tool for testing was power spectrum density (PSD) estimation by the Welch method, which allowed us to analyze the PSD of nonstationary signals.

Action Potentials↗

PubMed2006

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition.

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short perioda9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.

Action Potentials↗

PubMed2006

[Classification of surface EMG signal based on wavelet transform with nonlinear scale].

Surface EMG (sEMG) signal is a complex nonlinear, non-stationary signal. In this paper, wavelet transform with nonlinear scale (NWT) is introduced. Due to the gradual shortening of its time-resolution, NWT is good at extracting the precise time-frequency information from sEMG signal. First, every sEMG signal (30 sets are for forearm supination and 30 sets are for forearm pronation) is transformed into intensity distribution (time-frequency distribution) by NWT. And then the feature vector is determined from the characteristic roots which are obtained from the intensity distribution by principle component analysis. At last, the two patterns of sEMG signals are identified by BP neural network. The results show that the accurate classification rate is higher gained by NWT than by two conventional time-frequency distributions. At the same time, the calculating complexity of neural network is decreased greatly.

Electromyography↗

PubMed2005

Classification of surface EMG signal using relative wavelet packet energy.

Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is evaluated from several selected frequency bands of surface EMG signal. Compared with a conventional method, which is of the best performance in previous applications, the method can compress the interferential features and enhance the discriminable features more effectively. In consequence, the RWPE features calculated by the method represent different patterns of surface EMG signal more accurately and the accuracy of surface EMG signal pattern classification is improved greatly.

Electromyography↗

PubMed2004

Microarray transfection analysis of transcriptional regulation by cAMP-dependent protein kinase.

A wide variety of bioinformatic tools have been described to characterize potential transcriptional regulatory mechanisms based on genomic sequence analysis and microarray hybridization studies. However, these regulatory mechanisms are still experimentally verified using transient transfection methods. Current transfection methods are limited both by their large scale and by the low level of efficiency for certain cell types. Our goals were to develop a microarray-based transfection method that could be optimized for different cell types and that would be useful in reporter assays of transcriptional regulation. Here we describe a novel transfection method, termed STEP (surface transfection and expression protocol), which employs microarray-based DNA transfection of adherent cells in the functional analysis of transcriptional regulation. In STEP, recombinant proteins with biological activities designed to enhance transfection are complexed with expression vector DNAs prior to spotting on microscope slides. The recombinant proteins used in STEP complexes can be varied to increase the efficiency for different cell types. We demonstrate that STEP efficiently transfects both supercoiled plasmids and PCR-generated linear expression cassettes. A co-transfection assay using effector expression vectors encoding the cAMP-dependent protein kinase (PKA), as well as reporter vectors containing PKA-regulated promoters, showed that STEP transfection allows detection and quantitation of transcriptional regulation by this protein kinase. Because bioinformatic studies often result in the identification of many putative regulatory elements and signaling pathways, this approach should be of utility in high-throughput functional genomic studies of transcriptional regulation.

Animals↗